Proper Understanding of Condensed Nearest Neighbor
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I have a question regarding the Condensed Nearest Neighbors algorithm: 
Why am I returning Z, which if I understand correctly, is the array of all of the misclassified points? Wouldn't I want to return the points that were classified correctly? What benefit does this give me in returning all the points I got wrong?
algorithms dimensionality-reduction
asked 2 days ago
Jerry M.Jerry M.
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$begingroup$
I have a question regarding the Condensed Nearest Neighbors algorithm:
Why am I returning Z, which if I understand correctly, is the array of all of the misclassified points? Wouldn't I want to return the points that were classified correctly? What benefit does this give me in returning all the points I got wrong?
algorithms dimensionality-reduction
asked 2 days ago
Jerry M.Jerry M.
1083
New contributor
Jerry M. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have a question regarding the Condensed Nearest Neighbors algorithm:
Why am I returning Z, which if I understand correctly, is the array of all of the misclassified points? Wouldn't I want to return the points that were classified correctly? What benefit does this give me in returning all the points I got wrong?
algorithms dimensionality-reduction
asked 2 days ago
Jerry M.Jerry M.
1083
New contributor
Jerry M. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a question regarding the Condensed Nearest Neighbors algorithm:
Why am I returning Z, which if I understand correctly, is the array of all of the misclassified points? Wouldn't I want to return the points that were classified correctly? What benefit does this give me in returning all the points I got wrong?
algorithms dimensionality-reduction
algorithms dimensionality-reduction
asked 2 days ago
Jerry M.Jerry M.
1083
New contributor
Jerry M. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Jerry M.Jerry M.
1083
New contributor
Jerry M. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Jerry M.Jerry M.
1083
New contributor
Jerry M. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Jerry M.Jerry M.
1083
asked 2 days ago
Jerry M.Jerry M.
1083
1083
New contributor
Jerry M. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Jerry M. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Jerry M. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Condensed Nearest Neighbors algorithm helps to reduce the dataset X for k-NN classification. It constructs a subset of examples which are able to correctly classify the original data set using a 1-NN algorithm.
It is returning not the array of misclassified points, but a subset Z of the data set X.
CNN works like that:
1) Scan all elements of X, looking for an element x whose nearest prototype from Z has a different label than x
2) Remove x from X and add it to Z
3) Repeat the scan until no more prototypes are added to Z
Z used instead of X for kNN classification.
An advantage of this method is decreasing of execution time, reducing a space complexity
answered yesterday
Anastasiia ShalyginaAnastasiia Shalygina
661
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1 Answer
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1 Answer
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$begingroup$
Condensed Nearest Neighbors algorithm helps to reduce the dataset X for k-NN classification. It constructs a subset of examples which are able to correctly classify the original data set using a 1-NN algorithm.
It is returning not the array of misclassified points, but a subset Z of the data set X.
CNN works like that:
1) Scan all elements of X, looking for an element x whose nearest prototype from Z has a different label than x
2) Remove x from X and add it to Z
3) Repeat the scan until no more prototypes are added to Z
Z used instead of X for kNN classification.
An advantage of this method is decreasing of execution time, reducing a space complexity
answered yesterday
Anastasiia ShalyginaAnastasiia Shalygina
661
New contributor
Anastasiia Shalygina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Condensed Nearest Neighbors algorithm helps to reduce the dataset X for k-NN classification. It constructs a subset of examples which are able to correctly classify the original data set using a 1-NN algorithm.
It is returning not the array of misclassified points, but a subset Z of the data set X.
CNN works like that:
1) Scan all elements of X, looking for an element x whose nearest prototype from Z has a different label than x
2) Remove x from X and add it to Z
3) Repeat the scan until no more prototypes are added to Z
Z used instead of X for kNN classification.
An advantage of this method is decreasing of execution time, reducing a space complexity
answered yesterday
Anastasiia ShalyginaAnastasiia Shalygina
661
New contributor
Anastasiia Shalygina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Condensed Nearest Neighbors algorithm helps to reduce the dataset X for k-NN classification. It constructs a subset of examples which are able to correctly classify the original data set using a 1-NN algorithm.
It is returning not the array of misclassified points, but a subset Z of the data set X.
CNN works like that:
1) Scan all elements of X, looking for an element x whose nearest prototype from Z has a different label than x
2) Remove x from X and add it to Z
3) Repeat the scan until no more prototypes are added to Z
Z used instead of X for kNN classification.
An advantage of this method is decreasing of execution time, reducing a space complexity
answered yesterday
Anastasiia ShalyginaAnastasiia Shalygina
661
New contributor
Anastasiia Shalygina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Condensed Nearest Neighbors algorithm helps to reduce the dataset X for k-NN classification. It constructs a subset of examples which are able to correctly classify the original data set using a 1-NN algorithm.
It is returning not the array of misclassified points, but a subset Z of the data set X.
CNN works like that:
1) Scan all elements of X, looking for an element x whose nearest prototype from Z has a different label than x
2) Remove x from X and add it to Z
3) Repeat the scan until no more prototypes are added to Z
Z used instead of X for kNN classification.
An advantage of this method is decreasing of execution time, reducing a space complexity
answered yesterday
Anastasiia ShalyginaAnastasiia Shalygina
661
New contributor
Anastasiia Shalygina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered yesterday
Anastasiia ShalyginaAnastasiia Shalygina
661
New contributor
Anastasiia Shalygina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered yesterday
Anastasiia ShalyginaAnastasiia Shalygina
661
answered yesterday
Anastasiia ShalyginaAnastasiia Shalygina
661
661
New contributor
Anastasiia Shalygina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Anastasiia Shalygina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Anastasiia Shalygina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Jerry M. is a new contributor. Be nice, and check out our Code of Conduct.
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